Year: 2024
Type: article
Abstract: In the article face recognition is a widely researched field in computer vision, with various approaches being developed to improve accuracy and efficiency. One such approach is the use of probabilistic decision-based neural networks (PDBNN). In this article, the authors present a novel method for face recognition using PDBNNs. They explain that PDBNNs are a type of neural network that can model complex relationships between inputs and outputs, making them suitable for tasks like face recognition. The authors describe the architecture of their PDBNN, which consists of multiple layers of neurons. Each neuron in the network makes probabilistic decisions based on the inputs it receives, which are then propagated through the network to make a final decision. To train their PDBNN, the authors use a dataset of facial images with known labels. They describe a two-step training process, where the network is first trained using a standard backpropagation algorithm and then fine-tuned using a probabilistic decision-based learning algorithm. This allows the network to learn both discriminative features of the faces and the associated uncertainties. The author evaluate the performance of their PDBNN on several benchmark face recognition datasets.
Authors: Saida Beknazarova, Ozoda Abdullayeva, Safibullo Abdullayev, Zafarbek Abdullayev
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6959817640
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